デジタルツイン対応スマートエネルギーシステムのための空港準備態勢の戦略的管理：信頼性考慮型ベイズ決定フレームワーク

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel Bayesian approach to integrating data reliability into readiness assessments for energy transitions.

🏢実務担当者:Airport operators and infrastructure managers can use the framework to benchmark their digital-energy readiness and identify investment priorities.

🏛政策担当者:Regulators can reference the methodology for setting standards on sustainability disclosures and infrastructure transition planning.

Airports face growing pressure to decarbonize energy-intensive infrastructure while integrating digital twins, artificial intelligence, real-time monitoring, electrification, renewable-energy systems, and resilient operational controls. This study develops a reliability-aware Bayesian framework for assessing readiness for digital-twin-enabled smart-energy systems across 20 major international airports. The preferred latent model uses 15 airport-level capability indicators organized into four theory-defined domains: smart-energy infrastructure, digital twins and intelligent systems, managerial and organizational readiness, and governance and resilience. A sixteenth indicator, evidence transparency, is retained separately as an auxiliary reporting-quality measure. Eight additional variables describe the airport scale and country- or economy-level enabling environment. Evidence was compiled from official airport reports, sustainability disclosures, regulatory records, project documents, and international datasets, with explicit distinctions between operational systems, pilots, planned investments, and group-level claims. Observation-level reliability incorporates source quality, verification, temporal relevance, consistency, and completeness, while missing observations contribute no direct likelihood to the Bayesian measurement model. The preferred PyMC/NUTS specification produced stable posterior estimates with no divergent transitions, a maximum overall readiness R-hat of 1.003, and acceptable posterior predictive performance (RMSE = 0.094; MAE = 0.057). Singapore Changi, Hong Kong, Amsterdam Schiphol, and Copenhagen formed the highest-readiness group. Sensitivity analyses showed that the broad ordering was robust to alternative reliability assumptions, variance-floor choices, prior specifications, and high-information restrictions, although stronger informative-missingness assumptions affected several evidence-sparse airports. A complementary rule-based threshold screen identified capability shortfalls and evidence gaps without interpreting them as causal necessary conditions. The framework provides an uncertainty-aware basis for benchmarking, investment sequencing, procurement, and integrated digital and energy transition planning.

• semanticscholar https://doi.org/10.3390/en19174048first seen 2026-09-08 05:32:26 · last seen 2026-09-21 05:22:19